Deep kernel hedging
We introduce a deep kernel hedging framework that combines the flexibility of deep learning with the structural inductive bias of kernel methods. The hedging functional is restricted to a reproducing kernel Hilbert space whose kernel is parameterized through a neural network embedding of the input features. The framework minimizes a regularized empirical risk under convex loss functions and can accommodate path-dependent information through truncated time-augmented signature features. We derive a generalized representer theorem for the joint hedging problem, reducing the empirical optimization to a finite-dimensional problem. To further reduce the computational cost associated with large ...
| Authors | Jean-Loup Dupret, Donatien Hainaut, Edouard Motte |
|---|---|
| Primary category | cs.LG |
| Published | 28 September 2026 |
| Last updated at source | 14 October 2026 |
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